1. Field of the Art
The present invention generally relates to making recommendations in an online search, and more particularly to analyzing user authority for use in making recommendations for users of an on-demand database and/or application service.
2. Discussion of the Related Art
Performing a search online for a document is often simply a search for knowledge about a topic. Traditionally for an online search, a user types in keywords into a textbox and receives a results list with documents having the keywords. The documents in the results list can be sorted by the most relevant to the keywords, most popular (e.g., the number of incoming links to each document) and other factors for the user. The user then the scans results list and reads selected documents that might contain the information that he or she seeks.
This can be a time-consuming process for the user, especially if the user must scan many documents before finding one with the information sought. Keyword queries return documents that might be unrelated to the context of the search. For example, the keyword “apple” can refer to a fruit, computing machines produced by Apple Computer, Inc., the record company founded by the Beatles, etc. Even if contextual information is used to guess what the user refers to when he types “apple,” unrelated documents can still be returned. For example, the keyword “apple” in the context of a “computer” can refer to machines produced by Apple Computer, Inc. or to the company itself. The array of unrelated, useless documents returned in a search can frustrate users. If a user knew that his co-worker in the next cubicle over was an authority on a subject, then the user might simply opt to ask his co-worker about it. Likewise, if a user knew that there was someone with which he could speak, he might opt to speak with that person rather than sorting through a bunch of documents. After all, the user may be searching for information about how to do something rather than a particular document.
Many online communities and social networks both identify and award their top members as a means to incentivize and drive social behavior. The purpose of the identification and awards is to reward participation, promote top users, and recommend connections to accelerate viral effects in creating a social network. This has been enormously effective in organizations with voluntary memberships. This has been slow to catch on in corporate social business networks.
In a corporate social business network, the purpose of “connecting users” is to accelerate users' abilities to get their work done. In those situations, conventional user recommendations are of little value. The reason is that the recommendations are sometimes focused on the communities' social aspects rather than what is necessary to connect users to conduct business.
A better way of obtaining information in corporate social business networks, and other networks, is needed.